Commit message: Fix tf.raw_ops.LoadAndRemapMatrix vulnerability with invalid `row_remapping`.

Check that `row_remapping` has the correct dims().

PiperOrigin-RevId: 445522800
CVE: CVE-2022-29199
CWE: CWE-703
Vulnerability description: TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.LoadAndRemapMatrix does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `initializing_values` is a vector but there is no validation for this before accessing its value. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Project: tensorflow
File: load_and_remap_matrix_op.cc

  void Compute(OpKernelContext* context) override {
    // Checks what we're remapping and inverts the relevant remapping Tensors to
    // be maps with key = old ID, value = new ID.
    std::unordered_map<int64_t, int64_t> old_row_to_new_row_map;
    std::vector<bool> row_id_present;
    const Tensor* row_remapping_t;
    OP_REQUIRES_OK(context, context->input("row_remapping", &row_remapping_t));
    const auto row_remapping = row_remapping_t->vec<int64_t>();
    OP_REQUIRES(context, row_remapping.size() == num_rows_,
                errors::InvalidArgument(strings::StrCat(
                    "Size of row_remapping is ", row_remapping.size(),
                    " instead of being equal to num_rows=", num_rows_)));
    OP_REQUIRES_OK(context, RemapVectorToMap(row_remapping, &row_id_present,
                                             &old_row_to_new_row_map));

    // Calculates the min/max old row ID that we need to read, to save us from
    // reading some unnecessary slices of the old tensor.
    int64_t min_old_row = -1;
    int64_t max_old_row = -1;
    for (int i = 0; i < row_remapping.size(); ++i) {
      if (min_old_row < 0 ||
          (row_remapping(i) >= 0 && row_remapping(i) < min_old_row)) {
        min_old_row = row_remapping(i);
      }
      if (max_old_row < 0 ||
          (row_remapping(i) >= 0 && row_remapping(i) > max_old_row)) {
        max_old_row = row_remapping(i);
      }
    }

    // Processes the remapping for columns.
    std::unordered_map<int64_t, int64_t> old_col_to_new_col_map;
    std::vector<bool> col_id_present;
    const Tensor* col_remapping_t;
    OP_REQUIRES_OK(context, context->input("col_remapping", &col_remapping_t));
    const auto col_remapping = col_remapping_t->vec<int64_t>();
    // Note that we always "remap rows", even when the row vocabulary does
    // not change, because partitioning requires a mapping from partitioned
    // Variables to the full checkpoints we load.
    const bool remap_cols = col_remapping.size() > 0;
    if (remap_cols) {
      OP_REQUIRES(
          context, col_remapping.size() == num_cols_,
          errors::InvalidArgument(strings::StrCat(
              "Provided col_remapping, but its size is ", col_remapping.size(),
              " instead of being equal to num_cols=", num_cols_)));
      OP_REQUIRES_OK(context, RemapVectorToMap(col_remapping, &col_id_present,
                                               &old_col_to_new_col_map));
    } else {
      col_id_present.clear();
      col_id_present.resize(num_cols_, true);
    }

    // Processes the checkpoint source and the provided Tensor name.
    const Tensor* ckpt_path_t;
    OP_REQUIRES_OK(context, context->input("ckpt_path", &ckpt_path_t));
    OP_REQUIRES(
        context, ckpt_path_t->NumElements() == 1,
        errors::InvalidArgument("The `ckpt_path` tensor must have exactly one "
                                "element, got tensor of shape ",
                                ckpt_path_t->shape().DebugString()));
    const string& ckpt_path = ckpt_path_t->scalar<tstring>()();
    const Tensor* old_tensor_name_t;
    OP_REQUIRES_OK(context,
                   context->input("old_tensor_name", &old_tensor_name_t));
    const string& old_tensor_name = old_tensor_name_t->scalar<tstring>()();

    LOG(INFO) << "Processing checkpoint : " << ckpt_path;
    BundleReader reader(context->env(), ckpt_path);
    OP_REQUIRES_OK(context, reader.status());

    DataType tensor_type;
    TensorShape tensor_shape;
    OP_REQUIRES_OK(context, reader.LookupDtypeAndShape(
                                old_tensor_name, &tensor_type, &tensor_shape));
    OP_REQUIRES(context, tensor_type == DT_FLOAT,
                errors::InvalidArgument(strings::StrCat(
                    "Tensor ", old_tensor_name, " has invalid type ",
                    DataTypeString(tensor_type), " instead of expected type ",
                    DataTypeString(DT_FLOAT))));
    // This op is limited to loading Tensors of rank 2 (matrices).
    OP_REQUIRES(
        context, tensor_shape.dims() == 2,
        errors::InvalidArgument(strings::StrCat(
            "Tensor ", old_tensor_name, " has shape ",
            tensor_shape.DebugString(), " of invalid rank ",
            tensor_shape.dims(), " instead of expected shape of rank 2.")));

    if (!remap_cols) {
      // TODO(weiho): Consider relaxing this restriction to allow partial column
      // loading (even when no column remapping is specified) if there turns out
      // to be a use case for it.
      OP_REQUIRES(context, num_cols_ == tensor_shape.dim_size(1),
                  errors::InvalidArgument(strings::StrCat(
                      "Tensor ", old_tensor_name, " has shape ",
                      tensor_shape.DebugString(),
                      ", where the size of its 2nd dimension is ",
                      tensor_shape.dim_size(1),
                      " instead of being equal to num_cols=", num_cols_)));
    }

    // Uses TensorSlice to potentially load the old tensor in chunks in case
    // memory usage is a concern.
    std::vector<TensorSlice> tensor_slices;
    TensorSlice slice(tensor_shape.dims());
    if (min_old_row >= 0 && max_old_row >= 0) {
      int64_t row_start = min_old_row;
      // TODO(weiho): Given the list of old row IDs of interest (the keys of
      // old_row_to_new_row_map), we could also try something smarter to
      // find some minimal set of covering ranges for the list of old row IDs
      // such that the size of each range is less than max_rows_in_memory_.
      while (row_start <= max_old_row) {
        const int64_t slice_length =
            max_rows_in_memory_ <= 0
                // If max_rows_in_memory_ <= 0, we just load the entire chunk.
                ? max_old_row - row_start + 1
                : std::min(max_rows_in_memory_, max_old_row - row_start + 1);
        slice.set_start(0, row_start);
        slice.set_length(0, slice_length);
        tensor_slices.push_back(slice);
        row_start += slice_length;
      }
    }

    // Allocates the output matrix.
    Tensor* output_matrix_t = nullptr;
    OP_REQUIRES_OK(context,
                   context->allocate_output("output_matrix",
                                            TensorShape({num_rows_, num_cols_}),
                                            &output_matrix_t));
    auto output_matrix = output_matrix_t->matrix<float>();

    // Iterates through tensor slices and copies over values from the old tensor
    // to the output matrix.
    int64_t row_index = min_old_row;
    int64_t rows_copied = 0;
    Tensor loaded_tensor_t;
    for (const TensorSlice& tensor_slice : tensor_slices) {
      LOG(INFO) << "Loading slice " << tensor_slice.DebugString();
      TensorShape slice_shape;
      OP_REQUIRES_OK(context,
                     tensor_slice.SliceTensorShape(tensor_shape, &slice_shape));
      // Potentially re-allocates the tensor buffer since the last slice may
      // have fewer rows than the other slices.
      if (loaded_tensor_t.shape() != slice_shape) {
        loaded_tensor_t = Tensor(DT_FLOAT, slice_shape);
      }
      OP_REQUIRES_OK(context, reader.LookupSlice(old_tensor_name, tensor_slice,
                                                 &loaded_tensor_t));

      // Iterates through the old loaded tensor slice row-by-row.
      for (int row = 0; row < loaded_tensor_t.dim_size(0); ++row, ++row_index) {
        if (row_index % 500000 == min_old_row) {
          LOG(INFO) << "Processing old row " << row_index;
        }

        // If the old row ID is not found in old_row_to_new_row_map, continue
        // to the next row; otherwise, copy it to the output matrix.
        const int64_t* new_row_ptr =
            gtl::FindOrNull(old_row_to_new_row_map, row_index);
        if (new_row_ptr == nullptr) {
          continue;
        }
        ++rows_copied;
        const int64_t new_row = *new_row_ptr;

        // Copies over the row element-by-element, in case remapping is needed
        // along the column axis.
        const auto& loaded_tensor = loaded_tensor_t.matrix<float>();
        for (int old_col = 0; old_col < loaded_tensor_t.dim_size(1);
             ++old_col) {
          int64_t new_col = old_col;
          if (remap_cols) {
            const int64_t* new_col_ptr =
                gtl::FindOrNull(old_col_to_new_col_map, old_col);
            if (new_col_ptr == nullptr) {
              // Column remapping is specified, but this column is not found in
              // old_col_to_new_col_map, so we leave it uninitialized, to be
              // filled in with initializing_values later.
              continue;
            }
            new_col = *new_col_ptr;
          }

          OP_REQUIRES(context,
                      new_row < num_rows_ && new_col < num_cols_ &&
                          new_row >= 0 && new_col >= 0,
                      errors::Internal(strings::StrCat(
                          "new_row=", new_row, " and new_col=", new_col,
                          " should have been less than num_rows_=", num_rows_,
                          " and num_cols_=", num_cols_,
                          " and non-negative. This should never have happened "
                          "if the code were correct. Please file a bug.")));
          output_matrix(new_row, new_col) = loaded_tensor(row, old_col);
        }
      }
    }
    LOG(INFO) << "Copied " << rows_copied << " rows from old matrix (with "
              << tensor_shape.dim_size(0) << " rows) to new matrix (with "
              << num_rows_ << " rows).";

    // At this point, there are potentially whole rows/columns uninitialized
    // (corresponding to the indices where row_id_present/col_id_present are
    // false). We fill this in cell-by-cell using row_id_present and
    // col_id_present while dequeuing from the initializing_values vector.
    const Tensor* initializing_values_t;
    OP_REQUIRES_OK(
        context, context->input("initializing_values", &initializing_values_t));
    const auto initializing_values = initializing_values_t->flat<float>();
    int64_t initializing_values_index = 0;
    for (int i = 0; i < num_rows_; ++i) {
      for (int j = 0; j < num_cols_; ++j) {
        if (row_id_present[i] && col_id_present[j]) continue;
        OP_REQUIRES(
            context, initializing_values_index < initializing_values.size(),
            errors::InvalidArgument(
                "initializing_values contained ", initializing_values.size(),
                " elements, but more missing values remain."));
        output_matrix(i, j) = initializing_values(initializing_values_index);
        ++initializing_values_index;
      }
    }

    // Checks that we used all the given initializing values.
    OP_REQUIRES(
        context, initializing_values_index == initializing_values.size(),
        errors::InvalidArgument(
            "initializing_values contained ", initializing_values.size(),
            " elements, but only ", initializing_values_index,
            " elements were used to fill in missing values."));
  }